code: def evaluate(
      env,
      load_path="saved_files",
      logs_path=None,
      nb_episode=1,
      nb_process=1,
      max_steps=-1,
      verbose=False,
      save_gif=False,
      **kwargs,
  ):
      runner_params = env.get_params_for_runner()
      runner_params["verbose"] = verbose
  
      # Create the agent (this piece of code can change)
      agent = RLAgent(env.action_space)
  
      # Load weights from file (for example)
      agent.load(load_path)
  
      # Build runner
      runner = Runner(
          **runner_params, agentClass=None, agentInstance=agent
      )
  
      # you can do stuff with your model here
  
      # start the runner
      res = runner.run(
          path_save=logs_path,
          nb_episode=nb_episode,
          nb_process=nb_process,
          max_iter=max_steps,
          pbar=False,
      )
  
      # Print summary
      print("Evaluation summary:")
      for _, chron_name, cum_reward, nb_time_step, max_ts in res:
          msg_tmp = "\tFor chronics located at {}\n".format(chron_name)
          msg_tmp += "\t\t - cumulative reward: {:.6f}\n".format(
              cum_reward
          )
          msg_tmp += "\t\t - number of time steps completed: {:.0f} / {:.0f}".format(
              nb_time_step, max_ts
          )
          print(msg_tmp)
  
      if save_gif:
          save_log_gif(logs_path, res)
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/evaluate.py
imports: import torch
  import os
  from grid2op.Runner import Runner
  from l2rpn_baselines.utils.save_log_gif import save_log_gif
  from rl_agent import RLAgent
  import argparse
  import grid2op
  from lightsim2grid.LightSimBackend import LightSimBackend
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(env, load_path, logs_path, nb_episode, nb_process, max_steps, verbose, save_gif) without return types